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<body>
<pre><code class="r">##############################################################
##############################################################
### Replication Materials for
### Stefan Müller and Liam Kneafsey:
### Evidence for the Irrelevance of Irrelevant Events
### Political Science Research and Methods
###
### Please get in touch with the authors if you have any questions: 
### stefan.mueller@ucd.ie
### Note: 0000_readme.pdf contains 
### instructions and details about each script and dataset
##############################################################
##############################################################


## load packages
library(dplyr)   # CRAN v1.0.5
library(tidyr)   # CRAN v1.1.3
library(MuMIn)   # CRAN v1.43.17
library(stringr) # CRAN v1.4.0
library(cowplot) # CRAN v1.1.1
library(car)     # CRAN v3.0-10
library(ggplot2) # CRAN v3.3.3
library(here)    # CRAN v1.0.1

here::here()
</code></pre>

<pre><code>## [1] &quot;/Users/smueller/Dropbox/papers/ireland_incumbency_gaa&quot;
</code></pre>

<pre><code class="r">## alternatively, set working directory manually
## setwd(&quot;...&quot;)

source(&quot;function_theme_base.R&quot;)

## parts of this code are adjusted from
## https://dcosme.github.io/2019/02/26/specification-curve-example/

## load dataset with elections
dat_local_raw &lt;- readRDS(&quot;data_elections_local_full.rds&quot;) 


## only keep: incumbents whose county either lost or won a game
## = all &quot;treated&quot; incumbents
## the coefficienf for winner_loser_before_num will show 
## whether an incumbent increases her vote share when the county team
## won shortly before the election (winner_loser_before_num = 1)

dat_local_incumbents_treated &lt;- dat_local_raw %&gt;% 
    filter(incumbent == &quot;Incumbent (Candidate elected in t-1)&quot;) %&gt;% 
    filter(winner_loser_before != &quot;Untreated&quot;) %&gt;% 
    mutate(decade = factor(paste0(substr(election_id, 1, 3), &quot;0&quot;))) %&gt;% 
    mutate(winner_loser_before = as.character(winner_loser_before)) %&gt;% 
    mutate(winner_loser_before_num = ifelse(winner_loser_before == &quot;Win&quot;, 1, 0))


#                # create a dummy for strongholds
dat_local_incumbents_treated &lt;- dat_local_incumbents_treated %&gt;% 
    mutate(stronghold_dummy = ifelse(str_detect(stronghold_win_loss, &quot;Stronghold&quot;), 
                                     &quot;Stronghold&quot;, &quot;Non-stronghold&quot;))


## set na.action for dredge
options(na.action = &quot;na.fail&quot;)


## select only the variables included in the regression models 
## and omit missing values to have the same N across all models

dat_local_incumbents_treated_no_na &lt;- dat_local_incumbents_treated %&gt;% 
    ungroup() %&gt;% 
    select(diff_share, county_gaa, 
           winner_loser_before_num, turnout, 
           election_id, sport, margin,
           party_recoded, stronghold_dummy) %&gt;% 
    na.omit() 


summary(dat_local_incumbents_treated_no_na$winner_loser_before_num)
</code></pre>

<pre><code>##    Min. 1st Qu.  Median 
##  0.0000  0.0000  1.0000 
##    Mean 3rd Qu.    Max. 
##  0.5854  1.0000  1.0000
</code></pre>

<pre><code class="r">## number of complete observations in this subset
nrow(dat_local_incumbents_treated_no_na)
</code></pre>

<pre><code>## [1] 1423
</code></pre>

<pre><code class="r">## run full model
full_model_local &lt;- lm(diff_share ~ 
                           winner_loser_before_num +
                           county_gaa +
                           turnout +
                           margin +
                           election_id +
                           sport +
                           party_recoded +
                           stronghold_dummy,
                       data = dat_local_incumbents_treated_no_na)


## run all possible nested models but only keep thos models that include
## the treatment variable (winner_loser_before)

all_models_local &lt;-  dredge(full_model_local) 
</code></pre>

<pre><code>## Fixed term is &quot;(Intercept)&quot;
</code></pre>

<pre><code class="r">nrow(all_models_local)
</code></pre>

<pre><code>## [1] 256
</code></pre>

<pre><code class="r">## filter only models that include the main independent variable of interest (winner/loser)
all_models_local &lt;- filter(all_models_local, !is.na(winner_loser_before_num))

nrow(all_models_local)
</code></pre>

<pre><code>## [1] 128
</code></pre>

<pre><code class="r">## get coefficients and standard errors for all estimates in each model
all_models_local_coefs &lt;- coefTable(all_models_local)

length(all_models_local_coefs)
</code></pre>

<pre><code>## [1] 256
</code></pre>

<pre><code class="r">## transform list of coefficients and standard errors to a data frame
df_all_models_local_coefs &lt;- do.call(rbind.data.frame, all_models_local_coefs)

## get coefficient name from rownames
df_all_models_local_coefs$coefficient &lt;- rownames(df_all_models_local_coefs)


## filter only those coefficients relating to winner_loser_before_num
## because these values are needed to extract the standard errors of the coefficients
df_coefs_se_winner_loser_before &lt;- filter(df_all_models_local_coefs, 
                                          str_detect(coefficient, &quot;winner_loser&quot;)) 

#                # create variable for merging below
df_coefs_se_winner_loser_before &lt;- df_coefs_se_winner_loser_before %&gt;% 
    mutate(winner_loser_before_num = Estimate) 

## merge standard errors for the independent variable of interest
## by using the point estimates of winner_loser_before_num
## as the merging variable (not elegant, but not possible to do it differently)

## check that estimate colums are unique 
unique_valid_models &lt;- nrow(filter(all_models_local, !is.na(winner_loser_before_num)))
unique_valid_models
</code></pre>

<pre><code>## [1] 128
</code></pre>

<pre><code class="r">unique_coefs &lt;- length(unique(all_models_local$winner_loser_before_num))
unique_coefs
</code></pre>

<pre><code>## [1] 128
</code></pre>

<pre><code class="r">unique_coefs_se &lt;- length(unique(df_coefs_se_winner_loser_before$winner_loser_before_num))
unique_coefs_se
</code></pre>

<pre><code>## [1] 128
</code></pre>

<pre><code class="r">## this code works only if the number of unique 
## coefficients is the same in both dataframes!
## Be very careful if you adjust this code for your own work.

## stop the code if the numbers differ
stopifnot(unique_coefs_se == unique_coefs)


all_models_winner_loser_before &lt;- left_join(all_models_local,
                                            df_coefs_se_winner_loser_before,
                                            by = c(&quot;winner_loser_before_num&quot;))


nrow(all_models_winner_loser_before)
</code></pre>

<pre><code>## [1] 128
</code></pre>

<pre><code class="r">nrow(all_models_local)
</code></pre>

<pre><code>## [1] 128
</code></pre>

<pre><code class="r">## get variables included in the model
variable.names &lt;- names(dat_local_incumbents_treated_no_na)[-1]


## prepare data for plot
plot_data_local &lt;-  all_models_winner_loser_before %&gt;%
    filter(!is.na(Estimate)) %&gt;% ## remove models without coefficient for winner_loser_before_num
    arrange(Estimate) %&gt;% ## arrange estimates in ascending order
    mutate(specification = row_number()) 

nrow(plot_data_local) ## number of models
</code></pre>

<pre><code>## [1] 128
</code></pre>

<pre><code class="r">## check how many coefficients are positive/negative and how many are
## statistically significant

plot_data_local_check &lt;- plot_data_local %&gt;% 
    mutate(significant_95 = (Estimate - 1.96 * `Std. Error`) &gt; 0 |
               (Estimate + 1.96 * `Std. Error`) &lt; 0) %&gt;% 
    mutate(direction = ifelse(Estimate &gt; 0, &quot;Positive&quot;,
                              ifelse(Estimate &lt; 0, &quot;Negative&quot;, &quot;0&quot;)))


table(plot_data_local_check$direction,
      plot_data_local_check$significant_95)
</code></pre>

<pre><code>##           
##            FALSE TRUE
##   Negative    68   59
##   Positive     1    0
</code></pre>

<pre><code class="r">plot_top_local &lt;- plot_data_local %&gt;%
    ggplot(aes(x = specification, 
               y = Estimate)) + 
    geom_linerange(aes(ymin = Estimate - 1.96 * `Std. Error`,
                       ymax = Estimate + 1.96 * `Std. Error`),
                   colour = &quot;grey50&quot;) +
    geom_point(size = 1) +
    geom_hline(yintercept = 0, linetype = &quot;dashed&quot;, color = &quot;red&quot;) + ## dashed line for null model AIC
    labs(x = &quot;&quot;, y = &quot;Coefficient estimate for Winner&quot;)
plot_top_local
</code></pre>

<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-1"/></p>

<pre><code class="r">## note: warning will appear because not all models contain all variables 
## (therefore, the attributes are not identical and will be dropped)

plot_data_local_long &lt;-  plot_data_local %&gt;%
    gather(variable, value, eval(variable.names)) %&gt;% 
    mutate(value = ifelse(!is.na(value), &quot;|&quot;, &quot;&quot;)) ## plot a line if included in the model
</code></pre>

<pre><code>## Warning: attributes are not identical across measure variables;
## they will be dropped
</code></pre>

<pre><code class="r">## remove winner_loser_before_num because this variable is 
## included in all models and this coefficient is displayed in the graph

plot_data_local_long &lt;- plot_data_local_long %&gt;% 
    filter(variable != &quot;winner_loser_before_num&quot;)


table(plot_data_local_long$variable)
</code></pre>

<pre><code>## 
##       county_gaa 
##              128 
##      election_id 
##              128 
##           margin 
##              128 
##    party_recoded 
##              128 
##            sport 
##              128 
## stronghold_dummy 
##              128 
##          turnout 
##              128
</code></pre>

<pre><code class="r">recode_vars &lt;- c(&quot;&#39;county_gaa&#39;=&#39;County team&#39;;
                 &#39;difference&#39;=&#39;Days after match&#39;;
                 &#39;election_id&#39;=&#39;Election&#39;;
                 &#39;margin&#39;=&#39;Margin of result&#39;;
                 &#39;party_recoded&#39;=&#39;Party&#39;;
                 &#39;sport&#39;=&#39;Sport&#39;;
                 &#39;stronghold_dummy&#39;=&#39;Stronghold&#39;;
                 &#39;turnout&#39;=&#39;Turnout&#39;&quot;)



plot_data_local_long &lt;- plot_data_local_long %&gt;% 
    mutate(variable = car::recode(variable, recode_vars))


plot_bottom_local &lt;-   ggplot(plot_data_local_long,
                              aes(specification, variable)) +
    geom_text(aes(label = value), alpha = 1) +
    labs(x = &quot;Specification number&quot;, y = &quot;Variables&quot;)

plot_bottom_local
</code></pre>

<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-1"/></p>

<pre><code class="r">## combine both plots
plot_grid(plot_top_local, plot_bottom_local, ncol = 1, align = &quot;v&quot;,
          rel_heights = c(0.6, 0.4))
</code></pre>

<p><img 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" alt="plot of chunk unnamed-chunk-1"/></p>

<pre><code class="r">ggsave(&quot;fig_a16.pdf&quot;,
       width = 9, height = 5.5)

## script executed successfully on
date()
</code></pre>

<pre><code>## [1] &quot;Wed Jun 23 09:57:45 2021&quot;
</code></pre>

<pre><code class="r">sessionInfo()
</code></pre>

<pre><code>## R version 4.0.2 (2020-06-22)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS  10.16
## 
## Matrix products: default
## LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
## 
## locale:
## [1] en_IE.UTF-8/en_IE.UTF-8/en_IE.UTF-8/C/en_IE.UTF-8/en_IE.UTF-8
## 
## attached base packages:
## [1] grid      stats    
## [3] graphics  grDevices
## [5] utils     datasets 
## [7] methods   base     
## 
## other attached packages:
##  [1] forcats_0.5.1     
##  [2] haven_2.3.1       
##  [3] scales_1.1.1      
##  [4] Hmisc_4.4-2       
##  [5] Formula_1.2-4     
##  [6] lattice_0.20-41   
##  [7] parameters_0.13.0 
##  [8] MuMIn_1.43.17     
##  [9] ebal_0.1-6        
## [10] jtools_2.1.2      
## [11] lubridate_1.7.10  
## [12] broom.mixed_0.2.6 
## [13] broom_0.7.4       
## [14] lme4_1.1-26       
## [15] survey_4.0        
## [16] survival_3.2-7    
## [17] Matrix_1.3-2      
## [18] WeightIt_0.11.0   
## [19] cobalt_4.2.4      
## [20] car_3.0-10        
## [21] carData_3.0-4     
## [22] cowplot_1.1.1     
## [23] here_1.0.1        
## [24] modelsummary_0.8.0
## [25] ggeffects_1.0.1   
## [26] estimatr_0.30.2   
## [27] ggplot2_3.3.3     
## [28] stringr_1.4.0     
## [29] tidyr_1.1.3       
## [30] dplyr_1.0.5       
## [31] knitr_1.31        
## 
## loaded via a namespace (and not attached):
##   [1] readxl_1.3.1       
##   [2] uuid_0.1-4         
##   [3] backports_1.2.1    
##   [4] systemfonts_1.0.1  
##   [5] plyr_1.8.6         
##   [6] TMB_1.7.19         
##   [7] splines_4.0.2      
##   [8] TH.data_1.0-10     
##   [9] digest_0.6.27      
##  [10] htmltools_0.5.1.1  
##  [11] fansi_0.4.2        
##  [12] magrittr_2.0.1     
##  [13] checkmate_2.0.0    
##  [14] cluster_2.1.0      
##  [15] openxlsx_4.2.3     
##  [16] officer_0.3.16     
##  [17] sandwich_3.0-0     
##  [18] jpeg_0.1-8.1       
##  [19] colorspace_2.0-0   
##  [20] rvest_0.3.6        
##  [21] ggrepel_0.9.1      
##  [22] mitools_2.4        
##  [23] xfun_0.20          
##  [24] crayon_1.4.1       
##  [25] zoo_1.8-8          
##  [26] glue_1.4.2         
##  [27] kableExtra_1.3.1   
##  [28] gtable_0.3.0       
##  [29] emmeans_1.5.4      
##  [30] webshot_0.5.2      
##  [31] abind_1.4-5        
##  [32] annotater_0.1.3    
##  [33] mvtnorm_1.1-1      
##  [34] DBI_1.1.1          
##  [35] Rcpp_1.0.6         
##  [36] viridisLite_0.4.0  
##  [37] xtable_1.8-4       
##  [38] htmlTable_2.1.0    
##  [39] foreign_0.8-81     
##  [40] stats4_4.0.2       
##  [41] htmlwidgets_1.5.3  
##  [42] httr_1.4.2         
##  [43] RColorBrewer_1.1-2 
##  [44] ellipsis_0.3.2     
##  [45] pkgconfig_2.0.3    
##  [46] farver_2.1.0       
##  [47] nnet_7.3-15        
##  [48] utf8_1.2.1         
##  [49] tidyselect_1.1.1   
##  [50] labeling_0.4.2     
##  [51] rlang_0.4.11       
##  [52] reshape2_1.4.4     
##  [53] munsell_0.5.0      
##  [54] cellranger_1.1.0   
##  [55] tools_4.0.2        
##  [56] cli_2.5.0          
##  [57] generics_0.1.0     
##  [58] sjlabelled_1.1.7   
##  [59] evaluate_0.14      
##  [60] tables_0.9.6       
##  [61] zip_2.1.1          
##  [62] pander_0.6.3       
##  [63] purrr_0.3.4        
##  [64] nlme_3.1-152       
##  [65] mime_0.10          
##  [66] xml2_1.3.2         
##  [67] compiler_4.0.2     
##  [68] rstudioapi_0.13    
##  [69] curl_4.3           
##  [70] png_0.1-7          
##  [71] tibble_3.1.1       
##  [72] statmod_1.4.35     
##  [73] stringi_1.5.3      
##  [74] highr_0.8          
##  [75] gdtools_0.2.3      
##  [76] texreg_1.37.5      
##  [77] nloptr_1.2.2.2     
##  [78] markdown_1.1       
##  [79] vctrs_0.3.8        
##  [80] pillar_1.6.0       
##  [81] lifecycle_1.0.0    
##  [82] estimability_1.3   
##  [83] data.table_1.14.0  
##  [84] insight_0.14.0     
##  [85] flextable_0.6.3    
##  [86] R6_2.5.0           
##  [87] latticeExtra_0.6-29
##  [88] gridExtra_2.3      
##  [89] rio_0.5.16         
##  [90] codetools_0.2-18   
##  [91] boot_1.3-27        
##  [92] MASS_7.3-53        
##  [93] assertthat_0.2.1   
##  [94] rprojroot_2.0.2    
##  [95] withr_2.4.2        
##  [96] multcomp_1.4-16    
##  [97] mgcv_1.8-33        
##  [98] bayestestR_0.8.2   
##  [99] hms_1.0.0          
## [100] rpart_4.1-15       
## [101] coda_0.19-4        
## [102] minqa_1.2.4        
## [103] rmarkdown_2.6      
## [104] snakecase_0.11.0   
## [105] base64enc_0.1-3
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